Explainable AI in Production: A Neuro-Symbolic Model for Real-Time Fraud Detection

SHAP needs 30 ms to explain a fraud prediction. That explanation is stochastic, runs after the decision, and requires a background dataset you have to maintain at inference time. This article benchmarks a neuro-symbolic model that produces a deterministic, human-readable explanation in 0.9 ms — as a by-product of the forward pass itself — on the Kaggle Credit Card Fraud dataset. The speedup is 33×. The fraud recall is identical. The post Explainable AI in Production: A Neuro-Symbolic Model for Real-Time Fraud Detection appeared first on Towards Data Science .